Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data

作者：Kelly D. Myers, Joshua W. Knowles, David Staszak, Michael D. Shapiro, William H. Howard, Mrinal Yadava, David Zuzick, Latoya Williamson, Nigam H. Shah, Juan M. Banda, Joe Leader, William C. Cromwell, Ed Trautman, Michael F Murray, Seth J. Baum, Seth A. Myers, Samuel S. Gidding, Katherine Wilemon, Daniel J. Rader · 发表于：The Lancet Digital Health · 年份：2019 · DOI：10.1016/s2589-7500(19)30150-5 · 被引用次数：85 · 研究领域：Lipoproteins and Cardiovascular Health、Diabetes, Cardiovascular Risks, and Lipoproteins、Genetic Associations and Epidemiology

BACKGROUND: Cardiovascular outcomes for people with familial hypercholesterolaemia can be improved with diagnosis and medical management. However, 90% of individuals with familial hypercholesterolaemia remain undiagnosed in the USA. We aimed to accelerate early diagnosis and timely intervention for more than 1·3 million undiagnosed individuals with familial hypercholesterolaemia at high risk for early heart attacks and strokes by applying machine learning to large health-care encounter datasets. METHODS: We trained the FIND FH machine learning model using deidentified health-care encounter data, including procedure and diagnostic codes, prescriptions, and laboratory findings, from 939 clinically diagnosed individuals with familial hypercholesterolaemia (395 of whom had a molecular diagnosis) and 83 136 individuals presumed free of familial hypercholesterolaemia, sampled from four US institutions. The model was then applied to a national health-care encounter database (170 million individuals) and an integrated health-care delivery system dataset (174 000 individuals). Individuals used in model training and those evaluated by the model were required to have at least one cardiovascular disease risk factor (eg, hypertension, hypercholesterolaemia, or hyperlipidemia). A Health Insurance Portability and Accountability Act of 1996-compliant programme was developed to allow providers to receive identification of individuals likely to have familial hypercholesterolaemia in their prac...